With the advancement of Edge AI and autonomous systems, AI applications are increasingly subject to energy, latency and environmental constraints. Biological neural systems naturally adhere to these constraints and, as such are a source of inspiration. Spiking Neural Networks (SNNs) are a more detailed model of biological neural processing. Recent work shows that they perform well in object recognition and detection in general, and in Autonomous Driving tasks based on ranged LIDAR data in particular. However, these LIDAR-SNN approaches do not optimize for latency, as they require the entire frame to be scanned before processing. They also require large SNNs, limiting the energy efficiency achieved. To reach both low-latency and high energy efficiency in LIDAR object recognition, we develop a compact recurrent SNN. First, we propose and examine an open LIDAR labeled dataset by processing the point clouds from the KITTI Vision Benchmark. We then train our recurrent SNNs on this dataset and propose specific optimizations, including input encoding, sparse connectivity and truncation of error-backpropagation. With these optimizations, we show that compact recurrent SNNs can exceed the performance of classical RNNs like LSTMs and approach the performance of large non-spiking CNNs. Additionally, they significantly reduce latency by allowing early and online object classification before the end of the sequence.

MOTIVATION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where non-additive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype-phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the Project MinE dataset. Based on recent insight that regulatory regions harbor the majority of disease-associated variants, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective. RESULTS: Our approach identifies potentially ALS-associated promoter regions, and generally outperforms other classification methods. Test results support the hypothesis that non-additive combinations of variants contribute to ALS. Architectures and protocols developed are tailored toward processing population-scale, whole-genome data. We consider this a relevant first step toward deep learning assisted genotype-phenotype association in whole genome-sized data. AVAILABILITY AND IMPLEMENTATION: Our code will be available on Github, together with a synthetic dataset (https://github.com/byin-cwi/ALS-Deeplearning). The data used in this study is available to bona-fide researchers upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.